library(tidyverse)
library(janitor)
library(knitr)
library(gee)
library(MuMIn)
library(forcats)
library(knitr)
library(RLRsim) # for testing significance of random intercept
dat =
read.csv(file = '../frmgham2.csv') %>%
clean_names()
dim(dat)
glimpse(dat)
head(dat)
View(dat)
dat %>% select(sex) %>% head()
dat =
dat %>%
mutate(sex = factor(sex)) #%>% fct_recode(sex, "female" = "2", "male" = "1")
ANS: Yes, the proportion of smokers decreases with the age.
dat %>%
select(cursmoke, age) %>%
ggplot(aes(x = age, y = cursmoke)) +
geom_jitter(height = 0.1, alpha = 0.1) +
geom_smooth(lwd = 1.5) +
theme_bw()
ANS: There is a higher proportion of smoker among men compared to women as both age ,but there is no interaction between age and sex.
dat %>%
select(cursmoke, age, sex) %>%
ggplot(aes(x = age, y = cursmoke, group = sex, color = sex)) +
geom_jitter(height = 0.1, alpha = 0.1) +
geom_smooth(lwd = 1.5) +
theme_bw()
ANS: Yes, number of sigarets smoked per day stays constant for 30-50 years old and decreases with age after 50 years old.
dat %>%
select(cigpday, age) %>%
ggplot(aes(x = age, y = cigpday)) +
geom_jitter(height = 0.1, alpha = 0.1) +
geom_smooth(lwd = 1.5) +
theme_bw()
dat %>%
select(cigpday, age) %>%
filter(cigpday > 0) %>%
ggplot(aes(x = age, y = cigpday)) +
geom_jitter(height = 0.1, alpha = 0.1) +
geom_smooth(lwd = 1.5) +
theme_bw()
ANS: There is sex effect (men smoke higer number of sigarets per day than women across age), but there is no sex and age interaction.
dat %>%
select(cigpday, age, sex) %>%
ggplot(aes(x = age, y = cigpday, group = sex, color = sex)) +
geom_jitter(height = 0.1, alpha = 0.1) +
geom_smooth(lwd = 1.5) +
theme_bw()
dat %>%
select(cigpday, age, sex) %>%
filter(cigpday > 0) %>%
ggplot(aes(x = age, y = cigpday, group = sex, color = sex)) +
geom_jitter(height = 0.1, alpha = 0.1) +
geom_smooth(lwd = 1.5) +
theme_bw()
ANS: Proportion of smokers decreases with increase of systolic blood presure
dat %>%
select(cursmoke, sysbp) %>%
ggplot(aes(x = sysbp, y = cursmoke)) +
geom_jitter(height = 0.1, alpha = 0.1) +
geom_smooth(lwd = 1.5) +
theme_bw()
ANS: slightly higher sysbp for non-smokers
dat %>%
select(cursmoke, sysbp) %>%
mutate(cursmoke = factor(cursmoke)) %>%
ggplot(aes(y = sysbp, x = cursmoke)) +
geom_boxplot(outlier.colour = "white") +
geom_jitter(width = 0.1, alpha = 0.1) +
theme_bw()
ANS: Proportion of smokers decreases with increase of systolic blood presure; the proportion is higher for men (sex effect).
dat %>%
select(cursmoke, sysbp, sex) %>%
ggplot(aes(x = sysbp, y = cursmoke, group = sex, color = sex)) +
geom_jitter(height = 0.1, alpha = 0.1) +
geom_smooth(lwd = 1.5) +
theme_bw()
ANS: no differences in sysbp between male and female smokers and non-smokers
dat %>%
select(cursmoke, sex, sysbp) %>%
mutate(cursmoke = factor(cursmoke),
smoke_sex = interaction(cursmoke, sex)) %>%
ggplot(aes(y = sysbp, x = smoke_sex)) +
geom_boxplot(outlier.colour = "white") +
geom_jitter(width = 0.1, alpha = 0.1) +
theme_bw()
ANS: Proportion of smokers decreases with increase of diastolic blood presure for BP=100 ad then proportion increases again (latter could be due to not enough data)
dat %>%
select(cursmoke, diabp) %>%
ggplot(aes(x = diabp, y = cursmoke)) +
geom_jitter(height = 0.1, alpha = 0.1) +
geom_smooth(lwd = 1.5) +
theme_bw()
ANS: no difference
dat %>%
select(cursmoke, diabp) %>%
mutate(cursmoke = factor(cursmoke)) %>%
ggplot(aes(y = diabp, x = cursmoke)) +
geom_boxplot(outlier.colour = "white") +
geom_jitter(width = 0.1, alpha = 0.1) +
theme_bw()
ANS: Proportion of smokers decreases with increase of diastolic blood presure; the proprtions are higher for men (sex effect).
dat %>%
select(cursmoke, diabp, sex) %>%
ggplot(aes(x = diabp, y = cursmoke, group = sex, color = sex)) +
geom_jitter(height = 0.1, alpha = 0.1) +
geom_smooth(lwd = 1.5) +
theme_bw()
ANS: no difference
dat %>%
select(cursmoke, sex, diabp) %>%
mutate(cursmoke = factor(cursmoke),
smoke_sex = interaction(cursmoke, sex)) %>%
ggplot(aes(y = diabp, x = smoke_sex)) +
geom_boxplot(outlier.colour = "white") +
geom_jitter(width = 0.1, alpha = 0.1) +
theme_bw()
ANS: Proportion of smokers slightly decreases with increase of total cholesterol values
dat %>%
select(cursmoke, totchol) %>%
ggplot(aes(x = totchol, y = cursmoke)) +
geom_jitter(height = 0.1, alpha = 0.1) +
geom_smooth(lwd = 1.5) +
theme_bw()
ANS: no difference
dat %>%
select(cursmoke, totchol) %>%
mutate(cursmoke = factor(cursmoke)) %>%
ggplot(aes(y = totchol, x = cursmoke)) +
geom_boxplot(outlier.colour = "white") +
geom_jitter(width = 0.1, alpha = 0.1) +
theme_bw()
ANS: Proportion of smokers hasnonlinera relationship with total cholesterol for women; proprtions increases with increase in total cholesterol for men (sex by totchol interaction effect).
dat %>%
select(cursmoke, totchol, sex) %>%
ggplot(aes(x = totchol, y = cursmoke, group = sex, color = sex)) +
geom_jitter(height = 0.1, alpha = 0.1) +
geom_smooth(lwd = 1.5) +
theme_bw()
ANS: no difference
dat %>%
select(cursmoke, sex, totchol) %>%
mutate(cursmoke = factor(cursmoke),
smoke_sex = interaction(cursmoke, sex)) %>%
ggplot(aes(y = totchol, x = smoke_sex)) +
geom_boxplot(outlier.colour = "white") +
geom_jitter(width = 0.1, alpha = 0.1) +
theme_bw()